Utility of isocitrate dehydrogenase 1 as a serum protein biomarker for the early detection of non-small-cell lung cancer: A multicenter in vitro diagnostic clinical trial

Identification of biomarkers for the early detection of non-small cell lung cancer: a systematic review and meta-analysis

Lung cancer (LC) causes few symptoms in the earliest stages, leading to one of the highest mortality rates among cancers. Low-dose computerised tomography (LDCT) is used to screen high-risk individuals, reducing the mortality rate by 20%. However, LDCT results in a high number of false positives and is associated with unnecessary follow-up and cost. Biomarkers with high sensitivities and specificities could assist in the early detection of LC, especially in patients with high-risk features. Carcinoembryonic antigen (CEA), cytokeratin 19 fragments and cancer antigen 125 have been found to be highly expressed during the later stages of LC but have low sensitivity in the earliest stages. We determined the best biomarkers for the early diagnosis of LC, using a systematic review of eight databases. We identified 98 articles that focussed on the identification and assessment of diagnostic biomarkers and achieved a pooled area under curve of 0.85 (95% CI 0.82-0.088), indicating that the diagnostic performance of these biomarkers when combined was excellent. Of the studies, 30 focussed on single/antigen panels, 22 on autoantibodies, 31 on miRNA and RNA panels, and 15 suggested the use of circulating DNA combined with CEA or neuron-specific enolase (NSE) for early LC detection. Verification of blood biomarkers with high sensitivities (Ciz1, exoGCC2, ITGA2B), high specificities (CYFR21-1, antiHE4, OPNV) or both (HSP90α, CEA) along with miR-15b and miR-27b/miR-21 from sputum may improve early LC detection. Further assessment is needed using appropriate sample sizes, control groups that include patients with non-malignant conditions, and standardised cut-off levels for each biomarker.

The isocitrate dehydrogenase 1 is a potential prognostic indicator for non-small cell lung cancer patients

BACKGROUND

The serum isocitrate dehydrogenase 1(IDH1) level is significantly elevated in patients with non-small cell lung cancer (NSCLC) and has important clinical value as a marker for early diagnosis. This study examined the dynamic changes of serum IDH1 levels of patients with NSCLC undergoing surgery or medical treatment, to evaluate its potential prognostic value.

METHODS

The study cohort included 83 NSCLC patients who underwent surgery, 37 NSCLC patients who underwent medical treatment, 50 healthy controls, and 52 disease controls. Serum levels of IDH1 were assayed by enzyme-linked immunoassay. Tumor biomarkers including carcinoembryonic antigen, squamous cell carcinoma, neuron-specific enolase, CYFRA21-1, and pro-gastrin-releasing peptide-which are currently used in clinical practice-were measured by automatic immunoanalyzers.

RESULTS

Serum IDH1 was significantly higher in patients with NSCLC compared with healthy people or patients with benign lung diseases (p < 0.001). The area under the receiver operating characteristic curve for diagnosis and differential diagnosis were 0.897 and 0.879, respectively, which were superior to the five tumor markers. Serum IDH1 levels decreased in most patients after surgery, with the most dramatic changes in patients with stage I tumors compared with stage II and III. Analyses of changes in the serum IDH1 level of patients after receiving chemotherapy or targeted therapy revealed that for patients with progressive disease, serum IDH1 increased significantly after treatment; for patients with partial response or stable disease, it decreased steadily.

CONCLUSION

IDH1 has potential prognostic value and may be used as a marker for the monitoring of treatment efficacy.

Identification of novel autoantibody signatures and evaluation of a panel of autoantibodies in breast cancer

Tumor-associated autoantibodies (TAAb) could be serological tumor markers. This study aims to discover novel TAAb signatures for breast cancer (BC) detection. The protein microarray was used to identify candidate TAAb, which were further validated in 1197 sera from BC, benign breast diseases (BD), and healthy controls (HC) by enzyme-linked immunosorbent assay. In addition, 319 preoperative and postoperative sera were evaluated. A panel was determined using four different classifiers. Twelve TAAb were identified with frequencies of 15.8%-59.2%; their levels were significantly decreased in postoperative sera compared to those in preoperative sera (P < .05). A panel with six TAAb was developed and evaluated. The area under the curve (AUC) was 0.879 (74.3% sensitivity, 91.9% specificity) and 0.865 (69.7% sensitivity, 91.7% specificity) for distinguishing BC from HC in the training set and test set, respectively. The panel had an AUC of .884 (71.2% sensitivity, 90.5% specificity) for discriminating BC from BD. For identifying BC from all controls (HC+BD), the AUC was .916 (78.9% sensitivity, 90.2% specificity). The AUC of the panel was .920 and .934 for distinguishing stage I-II and age < 50 BC from HC, respectively. These identified TAAb have the potential to provide a non-invasive approach to detect BC.

Identification and Validation of a Prognostic 5-Protein Signature for Biochemical Recurrence Following Radical Prostatectomy for Prostate Cancer

Background: Biochemical recurrence (BCR) is an indicator of prostate cancer (PCa)-specific recurrence and mortality. However, there is a lack of an effective prediction model that can be used to predict prognosis and to determine the optimal method of treatment for patients with BCR. Hence, the aim of this study was to construct a protein-based nomogram that could predict BCR in PCa.

Methods: Protein expression data of PCa patients was obtained from The Cancer Proteome Atlas (TCPA) database. Clinical data on the patients was downloaded from The Cancer Genome Atlas (TCGA) database. Lasso and Cox regression analyses were conducted to select the most significant prognostic proteins and formulate a protein signature that could predict BCR. Subsequently, Kaplan–Meier survival analysis and Cox regression analyses were conducted to evaluate the performance of the prognostic protein-based signature. Additionally, a nomogram was constructed using multivariate Cox regression analysis.

Results: We constructed a 5-protein-based prognostic prediction signature that could be used to identify high-risk and low-risk groups of PCa patients. The survival analysis demonstrated that patients with a higher BCR showed significantly worse survival than those with a lower BCR (p < 0.0001). The time-dependent receiver operating characteristic curve showed that the signature had an excellent prognostic efficiency for 1, 3, and 5-year BCR (area under curve in training set: 0.691, 0.797, 0.808 and 0.74, 0.739, 0.82 in the test set). Univariate and multivariate analyses indicated that this 5-protein signature could be used as independent prognosis marker for PCa patients. Moreover, the concordance index (C-index) confirmed the predictive value of this 5-protein signature in 3, 5, and 10-year BCR overall survival (C-index: 0.764, 95% confidence interval: 0.701–0.827). Finally, we constructed a nomogram to predict BCR of PCa.

Conclusions: Our study identified a 5-protein-based signature and constructed a nomogram that could reliably predict BCR. The findings might be of paramount importance for the prediction of PCa prognosis and medical decision-making.

Subjects: Bioinformatics, oncology, urology.

Chronic nickel (II) exposure induces the stemness properties of cancer cells through repressing isocitrate dehydrogenase (IDH1)

BACKGROUND

Nickel is a component of biomedical alloys that is released during corrosion or friction and causes cytotoxicity, mutation, differentiation or even carcinogenesis in tissues. However, the mechanisms underlying the potential hazards of Nickel-containing alloys implanted in the human body by surgery remain uncertain.

OBJECTIVE

To study the effect of Ni(II) (NiCl_2•6H₂O) on cancer cells.

METHODS

A549 and RKO cells were treated with various concentrations of Ni(II) to determine the effect of Ni(II) on cellular viability using a CCK8 assay. Flow cytometry was performed to analyze the effect of Ni(II) on apoptosis and the cell cycle. Sphere-forming assays were conducted to examine the stemness properties of A549 and RKO cells. Western blotting was to evaluate the expression levels of SOX2, IDH1, HIF-1ɑ and β-catenin. The expression of isocitrate dehydrogenase (IDH1) in rectum adenocarcinoma (READ) was analyzed by Gene Expression Profiling Interactive Analysis (GEPIA). Kaplan-Meier analysis was used to calculate the correlation between survival and IDH1 expression.

RESULTS

Long-term exposure (120 days) to 100 µM Ni(II) significantly repressed cell proliferation, decreased colony formation and arrested the cell cycle at the G₀/G₁ phase. In addition, the stem-like traits of A549 and RKO cells were significantly augmented. Ni(II) also significantly decreased the protein expression of IDH1 and the synthesis rate of NAPDH, which competitively inhibited α-ketoglutarate (α-KG) generation. The downregulation of IDH1 not only promoted β-catenin accumulation in the cell nucleus in a HIF-1ɑ signaling-dependent manner but also induced the expression of the transcription factor SOX2 to maintain the stemness properties of cancer cells. Moreover, IDH1 expression negatively correlated with the clinicopathologic characteristics of READ.

CONCLUSION

These findings demonstrate that chronic and continuous release of Ni(II) to the microenvironment suppresses IDH1 expression and augments the stemness properties of cancer cells via the activation HIF-1ɑ/β-catenin/SOX2 pathway to enhance local tumor recurrence in patients with implanted Nickel-containing alloys at surgical sites.

Identification of a Novel Protein-Based Signature to Improve Prognosis Prediction in Renal Clear Cell Carcinoma

Background

Clear cell renal cell carcinoma (ccRCC) is one of the most common types of malignant adult kidney cancer, and its incidence and mortality are not optimistic. It is well known that tumor-related protein markers play an important role in cancer detection, prognosis prediction, or treatment selection, such as carcinoembryonic antigen (CEA), programmed cell death 1 (PD-1), programmed cell death 1 ligand 1 (PD-L1), and cytotoxic T lymphocyte antigen 4 (CTLA-4), so a comprehensive analysis was performed in this study to explore the prognostic value of protein expression in patients with ccRCC.

Materials and Methods

Protein expression data were obtained from The Cancer Proteome Atlas (TCPA), and clinical information were downloaded from The Cancer Genome Atlas (TCGA). We selected 445 patients with complete information and then separated them into a training set and testing set. We performed univariate, least absolute shrinkage and selection operator (LASSO) Cox analyses to find prognosis-related proteins (PRPs) and constructed a protein signature. Then, we used stratified analysis to fully verify the prognostic significance of the prognostic-related protein signature score (PRPscore). Besides, we also explored the differences in immunotherapy response and immune cell infiltration level in high and low score groups. The consensus clustering analysis was also performed to identify potential cancer subgroups.

Results

From the training set, a total of 233 PRPs were selected, and a seven-protein signature was constructed, including ACC1, AR, MAPK, PDK1, PEA15, SYK, and BRAF. Based on the PRPscore, patients could be divided into two groups with significantly different overall survival rates. Univariate and multivariate Cox regression analyses proved that this signature was an independent prognostic factor for patients (P < 0.001). Moreover, the signature showed a high ability to distinguish prognostic outcomes among subgroups, and the low score group had a better prognosis (P < 0.001) and better immunotherapy response (P = 0.003) than the high score group.

Conclusion

We constructed a novel protein signature with robust predictive power and high clinical value. This will help to guide the disease management and individualized treatment of ccRCC patients.

9G TestTM Cancer/Lung: A Desirable Companion to LDCT for Lung Cancer Screening

Simple Summary

Lung cancer is the most common cause of cancer-related deaths globally. Patients diagnosed at early-stage (0–I) have a higher survival rate than the metastasized stages (III–IV). Thus, there is great potential to reduce mortality by diagnosing lung cancer at stage 0~I through community screening. LDCT is a promising method, but it has a high false-positive rate. Therefore, a biomarker test that can be used in combination with LDCT for lung cancer screening to reduce false-positive rates is highly awaited. The present study evaluated the applicability of 9G test^TM Cancer/Lung test to detect stage 0~IV lung cancer. 9G test^TM Cancer/Lung test detects stage I, stage II, stage III, and stage IV cancers with the sensitivities of 77.5%, 78.1%, 67.4%, and 33.3%, respectively, at the specificity of 97.3%. These results indicate that the 9G test^TM Cancer/Lung can be used in conjunction with LDCT to screen lung cancer.

Abstract

A complimentary biomarker test that can be used in combination with LDCT for lung cancer screening is highly desirable to improve the diagnostic capacity of LDCT and reduce the false-positive rates. Most importantly, the stage I lung cancer detection rate can be dramatically increased by the simultaneous use of a biomarker test with LDCT. The present study was conducted to evaluate 9G test^TM Cancer/Lung’s sensitivity and specificity in detecting Stage 0~IV lung cancer. The obtained results indicate that the 9G test^TM Cancer/Lung can detect lung cancer with overall sensitivity and specificity of 75.0% (69.1~80.3) and 97.3% (95.0~98.8), respectively. The detection of stage I, stage II, stage III, and stage IV cancers with sensitivities of 77.5%, 78.1%, 67.4%, and 33.3%, respectively, at the specificity of 97.3% have never been reported before. The receiver operating characteristic curve analysis allowed us to determine the population-weighted AUC of 0.93 (95% CI, 0.91–0.95). These results indicate that the 9G test^TM Cancer/Lung can be used in conjunction with LDCT to screen lung cancer. Furthermore, obtained results indicate that the use of 9G test^TM Cancer/Lung with LDCT for lung cancer screening can increase stage I cancer detection, which is crucial to improve the currently low 5-year survival rates.

Utility of isocitrate dehydrogenase 1 as a serum protein biomarker for the early detection of non-small-cell lung cancer: A multicenter in vitro diagnostic clinical trial

We aimed to verify the expression status and diagnostic significance of isocitrate dehydrogenase 1 (IDH1) in non-small-cell lung cancer (NSCLC), especially during early stages. Serum IDH1 levels were measured by ELISA. A total of 1223 participants (660 patients with NSCLC, 276 healthy controls [HCs], 95 patients with benign pulmonary conditions [BPCs], 135 patients with other cancers [OCs], and 57 samples with interfering factors) were divided into a training cohort and a validation cohort according to 3 testing centers. The IDH1 concentrations in the NSCLC group were obviously higher than those in the control groups (P < .001). Area under the receiver operating characteristic curves (AUCs) for discriminating NSCLC patients from controls (HC, BPC, and OC) were 0.870 and 0.745 (sensitivity, 63.3% and 55.0%; specificity, 86.8% and 86.3%) in the training cohort and validation cohort, respectively. The AUCs for discriminating stage 0-IA lung cancer patients from HCs were 0.907 and 0.788 (sensitivity, 58.6% and 59.1%; specificity, 92.9% and 89.3%) in 2 cohorts, respectively. Isocitrate dehydrogenase 1 showed specificity for NSCLC and had no diagnostic value for other common cancers. Furthermore, IDH1 was significantly reduced in postoperative serum. Isocitrate dehydrogenase 1 shows clinical utility as a serum protein biomarker for the early diagnosis of NSCLC.

Construction and Validation of a Protein Prognostic Model for Lung Squamous Cell Carcinoma

Lung squamous cell carcinoma (LUSCC), as the major type of lung cancer, has high morbidity and mortality rates. The prognostic markers for LUSCC are much fewer than lung adenocarcinoma. Besides, protein biomarkers have advantages of economy, accuracy and stability. The aim of this study was to construct a protein prognostic model for LUSCC. The protein expression data of LUSCC were downloaded from The Cancer Protein Atlas (TCPA) database. Clinical data of LUSCC patients were downloaded from The Cancer Genome Atlas (TCGA) database. A total of 237 proteins were identified from 325 cases of LUSCC patients based on the TCPA and TCGA database. According to Kaplan-Meier survival analysis, univariate and multivariate Cox analysis, a prognostic prediction model was established which was consisted of 6 proteins (CHK1_pS345, CHK2, IRS1, PAXILLIN, BRCA2 and BRAF_pS445). After calculating the risk values of each patient according to the coefficient of each protein in the risk model, the LUSCC patients were divided into high risk group and low risk group. The survival analysis demonstrated that there was significant difference between these two groups (p= 4.877e-05). The area under the curve (AUC) value of the receiver operating characteristic (ROC) curve was 0.699, which suggesting that the prognostic risk model could effectively predict the survival of LUSCC patients. Univariate and multivariate analysis indicated that this prognostic model could be used as independent prognosis factors for LUSCC patients. Proteins co-expression analysis showed that there were 21 proteins co-expressed with the proteins in the risk model. In conclusion, our study constructed a protein prognostic model, which could effectively predict the prognosis of LUSCC patients.